Stage: Query Execution Time Prediction in Amazon Redshift
Summary: Stage is a hierarchical predictor for Redshift, combining a cache, a per-instance light model with uncertainty, and a global transferable model. It mitigates cold starts and workload shifts, delivering ~20% lower latency with practical inference and memory. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ziniu Wu (Massachusetts Institute of Technology)
- 2. Ryan Marcus (University of Pennsylvania)
- 3. Zhengchun Liu (Amazon)
- 4. Parimarjan Negi (Massachusetts Institute of Technology)
- 5. Vikram Nathan (Amazon)
- 6. Pascal Pfeil (Amazon)
- 7. Gaurav Saxena (Amazon)
- 8. Mohammad Rahman (Amazon)
- 9. Balakrishnan Narayanaswamy (Amazon)
- 10. Tim Kraska (Amazon; Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{wu_sigmod24,
title = {{Stage: Query Execution Time Prediction in Amazon Redshift}},
author = {Wu, Ziniu and Marcus, Ryan and Liu, Zhengchun and Negi, Parimarjan and Nathan, Vikram and Pfeil, Pascal and Saxena, Gaurav and Rahman, Mohammad and Narayanaswamy, Balakrishnan and Kraska, Tim},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626246.3653391},
url = {https://dl.acm.org/doi/10.1145/3626246.3653391},
year = {2024}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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